Prompt Model

Prompt engineering, the art of crafting effective input instructions for large language models (LLMs) and other foundation models, aims to improve model performance and generalization across diverse tasks. Current research focuses on developing robust prompting strategies, including methods for refining low-quality prompts, generating supplementary prompts (e.g., point prompts alongside box prompts), and leveraging pre-trained models for prompt generation and matching. This field is crucial for advancing applications in various domains, such as medical image analysis, spatio-temporal prediction, and automated essay scoring, by enabling efficient and effective model utilization with minimal training data or task-specific adaptation. Furthermore, research addresses biases inherent in prompt-based approaches and seeks to improve fairness and generalizability.